Model-based feature classification and change detection

Describes a novel image processing system which performs constant false-alarm rate (CFAR) change detection and model-based feature classification on temporally separated and precision registered images. The authors present results of algorithms performed on 0.5 m to 2 m resolution scanned aerial photographs of the Yuma, Arizona region to identify changes that correspond to man-made targets and new roads and to classify false changes.<
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